Competitions IJCNN 2021

 

Title Competition 1:

COVID19 Detection in Blood Exams

Organizers:

Victor Henrique Alves Ribeiro

Artificial Intelligence Engineer

Hilab

Curitiba, Brazil

[email protected]

 

Marcus Vin´ıcius Mazega Figueredo

CEO

Hilab

Curitiba, Brazil

[email protected]

 

 

Abstract

The world currently suffers from global COVID19 pandemic. Billions of people have been impacted, and millions of casualties have already occurred. Nevertheless, such numbers continue to increase as a new vaccine is still not made available. Therefore, it is of extreme importance to identify individuals who are or have been contaminated by the SARS-CoV-2 virus. Such an identification aids public health organizations and governments to plan actions to reduce the impacts of such a pandemic. In such a sense, Hilab is a remote laboratory company that performs dozens of types of blood exams, including serology tests for COVID19, where millions of exams have already been performed by the company in Brazil. To improve the detection of such a virus, machine learning methods can be used to aid laboratory experts in the decision-making process. Therefore, this competition poses the difficult problem of building machine learning models with high confidence and accuracy for the detection of COVID19.

 Please see more details HERE

Link to website https://hilab.com.br/competition/

 Title Competition 2:

AI Challenge of Alzheimer's disease classification based on multicenter DTI data

Organizers:

Yong Liu, Institute of Automation, Chinese Academy of Sciences

Yida Qu, Institute of Automation, Chinese Academy of Sciences

Dawei Wang, Qilu Hospital of Shandong University

Pan Wang, Tianjin Huanhu Hospital

Hongxiaong Yao, Chinese PLA General Hospital

Bo Zhou, Chinese PLA General Hospital

Abstract: 

 Diffusion tensor imaging (DTI) has been widely used to show structural integrity and delineate white matter degeneration in AD through diffusion properties. It is confirmed that WMintegrity measures are effective in classifying AD using machine learning.

This project aims to evaluate and develop an analysis framework for optimal performance of Alzheimer's disease classification (AD vs normal controls, NC) using diffusion measurements (e.g. FA/MD, etc.) along18 major white matter tracts. The goal is to achieve both overall high prediction accuracy in cross-validated samples and high consistency in different sites. Secondly, an exploratory goal of this project is to investigate that to what extent can these measurements contribute to the prediction of mild cognitive impairment(MCI), that is, the classification of MCI.

Please see more details HERE

Link to website https://github.com/YongLiuLab/AI4AD_AFQ